Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN

Zafar, Muhammad Mohsin; Rauf, Zunaira; Sohail, Anabia; Khan, Abdul Rehman; Obaidullah, M; Khan, Saddam Hussain; Lee, Yeon Soo; Khan, Asifullah

doi:10.1016/j.pdpdt.2021.102676

Cited by 32 publications

(14 citation statements)

References 28 publications

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“…DL architectures have shown excellent performance in the medical and commercial fields [ 21 , 22 , 23 , 24 , 25 ]. Therefore, DL is primarily employed in the detection of COVID-19 infection and drug repurposing in diverse ways [ 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

Khan

2022

Diagnostics

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COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

Khan

2022

Diagnostics

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“…However, this is related to another issue concerning the missing information on both diagnostic and prognostic outcomes associated with patients (e.g., it is only known that the patients had HER2+ or TNBC immunophenotype, but the distribution was not specified; moreover, no prognostic outcomes were provided), which prevented us from performing diagnostic/prognostic predictions using the TIL assessment obtained using the developed classification method. Moreover, also based on previous studies tried to assess TILs using immunohistochemistry (IHC) images [ [61] , [62] , [63] ], it could be interesting to investigate pathomic features from IHC to add value to the developed approach. While TIL scoring in conventional H&E sections has the advantage that no additional stains are required, lymphocyte subtyping into cytotoxic, helper, regulatory, and other T cells not possible in H&E may become clinically relevant in the future.…”

Section: Discussionmentioning

confidence: 99%

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images

Mario¹,

Brancato²,

Cavaliere³

et al. 2023

Heliyon

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“…In medical image analysis, many research horizons are explored. These include various areas of medical imaging, such as detection, classification, and segmentation [ 4 , 5 , 6 , 7 , 8 , 9 ]. As cohorts build for brain tumor classification, there is a gap for novel approaches related to feature extraction by using limited and class-unbalanced MR images datasets of brain tumors and tumors from other parts of the human body [ 10 , 11 ].…”

Section: Related Workmentioning

confidence: 99%

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

Zahoor

Qureshi

Bibi

et al. 2022

Sensors

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Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

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Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN

Cited by 32 publications

References 28 publications

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

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